Machine Learning based Sorting of Somatic Embryos for In-Line processing in Automated SE Fluidics System

Abstract

Somatic embryogenesis (SE) can be a viable method for the clonal propagation of many economically significant forest trees, particularly coniferous trees like pines and spruces. However, large-scale production of SE plants requires automation to reduce manual labor and attain cost-efficiency. The most labor-intensive step of the SE process for SE plant production is selecting and harvesting mature embryos. Embryo maturation is not a synchronized process; selecting the most developed embryos capable of continuous development is necessary. However, there needs to be more research conducted on mapping morphological features to germination-competent mature somatic embryos. This paper lays down the preliminary work of employing machine learning techniques for classifying large volumes of images of mature somatic embryos processed using an automated SE processing system based on fluidics processing referred to as SE Fluidics system. The results show that machine learning could be an alternative classification methodology instead of the traditional manual morphology-based classification process based on image analysis. The paper discusses two popular image classification techniques, namely Convolution Neural Network (CNN) and Support Vector Machine (SVM), applying them to both binary (black and white) and grayscale images. It is observed that grayscale images provide better accuracy with the SVM technique and outperform morphology-based classification in terms of processing speed (17.6% faster) across the test envelope. On the other hand, CNN-based classification shows better processing speeds only at a lower number of convolution layers. Hence, the data scientist can optimally select the number of convolution layers to get the desired accuracy-processing speed combination.